Automation and the Rebirth of American Retail
In 2017, we mourned the death of American retail. From kitschy regional retailers to culturally ubiquitous icons like RadioShack and Toys R Us, the slaughter penetrated every corner of the sector, and the survivors witnessed uncharacteristically low sales, down 0.3% YTD, even during peak holiday periods. Sensationalist headlines claimed that this was the result of runaway capitalism, as the massive debt that private equity firms had saddled on these otherwise profitable companies came home to roost. Others took a more Darwinist view to the events, believing that Amazon was an altogether new kind of predator, one that would outcompete the retail establishment. It’s true that the ecommerce paradigm shift has sunk many of the industries relics, but it has also forced many others to swim.
If we viewed retail as an ecosystem, Walmart and Amazon would sit at the top. Extensive distribution networks, bottomed-out costs structures and effective sourcing has shaped these two into apex predators, both vying for the role as the primary retailer. At the base of the pyramid are those firms that couldn’t compete: HH Gregg, RadioShack, Kmart, etc. Whether they were slow to implement ecommerce platforms, unresponsive to shifting customer preferences or had uncompetitive cost structures, these firms are all falling by the wayside. Where consumers, investors, and Amazon should take notice is the middle of the food chain, where retailers like Home Depot, Best Buy, and Target are adapting to the new marketplace and even gaining ground.
In a joint study, comScore and UPS find that consumers make the majority of their purchases online. For giants like Amazon and Walmart, who each invest over $2B per year in their ecommerce and fulfillment centers, this would seem to provide an even greater advantage over the rest of the market, but new applications of automation and machine learning have allowed smaller retailers to level the playing field. But as rising labor costs (up to 7% in the last year in several major facilities) squeeze out the less competitive, inventory management and automation systems have helped Home Depot, Wayfair and Best Buy to realize upwards of 30% savings on labor and maintenance costs, with 17% increases in order fulfillment speeds.
Furthermore, brick and mortar establishments have an edge on ecommerce giants in the sales of home appliance, technology, and luxury products, and strategic leveraging of machine learning technology can further maximize this competitive edge. While upwards of 70% of American consumers prefer shopping at Amazon than at any other retailer, brick-and-mortar stores remain primary location for the sale of tv’s, washing machines, laptops, and other more expensive purchases. Machine learning can enhance the power of the storefront and make distribution networks significantly more effective. By using customer preferences to sort inventory by demand volume, retailers can build a dynamic system where the most desirable, quickest selling goods leave distribution facilities faster, gaining an edge on maintaining delivery to the storefronts and fulfilling ecommerce orders.
For decades, retailers maintained a distribution structure that was oriented around minimizing costs, so when Amazon presented customers with rapid delivery options and responsive customer service, any competitor that was slow to adapt fell victim to the changing paradigm. Collectively, this was taken as a sign of traditional retail’s eventual demise, but it instead became culled, where the slow and misfortunate were filtered out, and those willing to invest in automation and machine learning were reborn.
Written by Chasen Richards, Edited by Jack Vasquez & Alexander Fleiss
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